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Toxicity data for: COLLEMBOT: AI-based counting of Collembola for OECD 232 Tests
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This dataset provides raw and processed data from soil toxicity tests comparing manual and automated counting methods for springtails exposed to various contaminants, including polystyrene microplastics. The COLLEMBOT system uses AI-based image analysis to count test organisms, aiming to improve efficiency and reduce human error in standardized ecotoxicological assays. The data support validation of automated approaches for assessing microplastic toxicity in soil organisms.
This dataset is part of a scientific article in preparation. Currently submitted to Environmental Toxicology & Chemistry. This dataset contains raw and processed data from laboratory toxicity tests on Folsomia candida (Collembola) exposed to various soil contaminants under controlled conditions. The study compares traditional manual counting of juveniles and adults with automated image-based counting using the COLLEMBOT system. Data were collected across multiple soils (LUFA 2.2, OECD variants) and exposure scenarios, including different concentrations of pesticides (e.g., imidacloprid, chlorpyrifos, lindane), fungicides (fluazinam, cyproconazole), microplastics (polystyrene), and reference substances (boric acid). The dataset includes: Raw observations: survival and reproduction endpoints per replicate. Manual vs automated counts: paired measurements for validation of automated image analysis. Metadata: compound identity, soil type, concentration (nominal and adjusted), replicate information. Dose-response modeling script: ECx values (ED10–ED90), NOEC/LOEC estimates, and statistical comparisons between counting methods. This resource supports ecotoxicological research, automation in soil toxicity testing, and reproducibility in environmental risk assessment workflows. This repository also contains the model weights for COLLEMBOT under a AGPL 3.0 license with the code being available at GitHub under a MIT license: https://github.com/waldstrom/collembot
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Toxicity data for: COLLEMBOT: AI-based counting of Collembola for OECD 232 Tests
AI summary Read the abstract
This dataset accompanies research comparing traditional manual counting with an AI-based automated system (COLLEMBOT) for counting springtails in standardized soil toxicity tests. The experiments included exposure to various contaminants including polystyrene microplastics across different soil types. The data provide a resource for validating automated approaches to ecotoxicological testing that could improve the efficiency and reproducibility of microplastic toxicity assessments.
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Researchers used machine learning image analysis to automate the counting of springtail (Folsomia candida) mortality and reproduction in microplastic toxicity tests, replacing time-intensive manual counting. The automated approach matched manual counts in accuracy while reducing labor requirements for standard ecotoxicology assays.
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Researchers developed a machine learning approach to automate measurement of mortality and reproduction in the springtail Folsomia candida exposed to micro- and nanoplastics, reducing the time and manual effort required for standard soil ecotoxicity testing. The machine learning model accurately reproduced manual counts, enabling faster and more scalable testing of soil plastic pollution.
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This doctoral thesis investigated the ecotoxicology of mulching film-derived microplastics on Collembola (soil springtails) in agricultural soil, testing their effects both in single-species assays and in mesocosm experiments designed to reflect more realistic ecosystem-level conditions.
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